(1) Field of Invention
The present invention relates to sensing frameworks for surveillance and, more specifically, to a “blind” sensing system for surveillance that automatically separates signals from clutter for subsequent classification using statistical independence without prior knowledge of the signals.
(2) Description of Related Art
Many existing platforms must perform signal analysis and recognition tasks in challenging environments where signals of interest can be weak and buried in strong background clutter and interference, For example, it is often difficult for automotive radars to separate objects of interest from strong clutter, or track many objects at once, due to the limited number of emitting and receiving elements. Similarly, in electronic warfare adversary signals are often weak, spread out in frequency, and occupy the same frequency-time locations as other stronger signals. Further, within the field of maritime surveillance, a common method for extracting signals from clutter involves using prior knowledge to perform filtering before signal detection and recognition. One disadvantage of current methods is that they cannot easily handle signals overlapping in space and wavelength. Yet another disadvantage of these methods lies in their inability to separate new signals from clutter that has never been seen before.
Another method for extracting signals from clutter involves angle of arrival separation using phased array antennas for applications in the radio frequency (RE) domain. With this method, however, signal and clutter often overlap in angle as well. Yet another disadvantage to this method lies in the fact that even when the signal and method are separable in angle, steerable phased arrays are expensive and heavy compared to single element sensors.
In another related method, waveform coding is often used by automotive radars to separate signal from clutter, A disadvantage to waveform coding lies in the fact that performance can degrade in dense environments with many objects.
Blind source separation (hereinafter BSS) is another technique for signal detection. Although several BSS approaches have been developed within the prior art, each one of these uses the same or dependent signal dimensions for both separating the signals and analyzing them. One existing family of approaches for BSS is independent component analysis (hereinafter ICA). In ICA, different mixtures of signals are formed during the measurement process. For example, multiple microphones may record mixtures of voices in different ratios based on their locations. The mixtures are then processed using algorithms that maximize the statistical independence of the separated signals. In the case of one-dimensional signals, such as audio or EEG signals, both time domain and frequency domain ICA has been demonstrated. Frequency domain ICA has been used to separate convolutive mixtures in which the mixtures contain signals delayed by various amounts. In these cases the two domains are not independent because of frequency and time are conjugate variables, a consequence of the one-dimensional nature of the signals. The sparsity of signals has also been used for BSS using a single sensor, but many signals of interest are not sparse and the best basis set for sparsity can vary greatly for different signals.
With respect to demixing hyperspectral signals, existing hyperspectral ICA demixing methods are based on treating the hyperspectral data cube as a set of images with one image per spectral band. Therefore, it has been natural to approach hyperspectral ICA analysis as a straight extension of ICA analysis of conventional images, which leads to mixing and demixing in the same signal dimensions. While functional for signal separation, such existing processes are slow and inefficient, especially when some signal components are much stronger than others.
Thus, a continuing need exists for a sensing system that separates and classifies signals, including multi-dimensional data, while improving upon speed and computational efficiency.